DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 21-40 are presented for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21-40 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claims 21, 33, 39, it is unclear how the steps of obtaining driving log data…. And training a machine-learned model.. are performed. Where is the driving log data is obtained from?
Claims 22-32, 34-38, 40 are also rejected for incorporating the deficiencies of their base claim.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 21-22, 25-34, 36-40 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tiwari et al. (U.S. Pub No. 20180154899).
Regarding claims 21, 33, 39, Tiwari et al. disclose a computer-implemented method and one or more non-transitory computer-readable media that store instructions for execution by one or more processors to cause the one or more processors comprising: obtaining driving log data associated with a training object in an environment of a vehicle (See abstract); generating training data 0021 comprising the driving log data and one or more first labels for the driving log data, the first labels respectively indicative of whether the training object is blocking a travel way at respective time steps and at respective locations associated with a trajectory of the vehicle (See paragraph 0022, 0029, 0031); and training a machine-learned model using the training data, wherein the trained machine- learned model is configured to be executed by an autonomous vehicle operating within the environment (See abstract; paragraph 0014, 0021).
Regarding claims 22, 34, Tiwari et al. disclose wherein the driving log data is based on sensor data acquired by one or more sensors located on the vehicle as the vehicle travels on the travel way (See paragraph 0019, 0032).
Regarding claim 25, Tiwari et al. disclose wherein the machine-learned model is configured to make a blocking decision for a particular time step based at least in part on a blocking decision determined for one or more previous time steps (See paragraph 0029, 0031, 0028).
Regarding claim 26, Tiwari et al. disclose wherein the first labels for the driving log data are human-labeled (See 0014; manual operation mode includes a human operator performing actions associated with vehicle control by selecting tasks blocks).
Regarding claim 27, Tiwari et al. disclose wherein the first labels for the driving log data are machine-labeled (See paragraph 0014; autonomous operation mode considered performed actions to decision making block and controlling the vehicle).
Regarding claim 28, Tiwari et al. disclose wherein the training object comprises a pedestrian or another vehicle (See paragraph 0035).
Regarding claims 29, 36, Tiwari et al. disclose wherein the training data comprises one or more second labels for the driving log data, the second labels for the driving log data comprising a vehicle action at the respective time steps (See paragraph 0014; the autonomous operation mode and manual operation mode re considered as the first and second labels for the driving log data).
Regarding claim 30, Tiwari et al. disclose wherein the vehicle action at the respective time steps is determined as one of a pass action or a queue action (See paragraph 0044; at paragraph 0050, it is considered as the vehicle navigate it is a vehicle action determined as a pass action).
Regarding claims 31, 37, Tiwari et al. disclose training a machine-learned vehicle action model using the training data including the one or more second labels, wherein the trained machine-learned vehicle action model is configured to be executed by the autonomous vehicle operating within the environment (See paragraph 0014; the autonomous operation mode and manual operation mode are considered as the first and second labels for the driving log data; paragraph 0016 trained machine model learned vehicle action model executed by autonomous vehicle within the environment).
Regarding claims 32, 38, Tiwari et al. disclose wherein the machine-learned vehicle action model is configured to determine a vehicle action sequence comprising respective discrete vehicle actions with respect to an object in the environment of the autonomous vehicle (See paragraph 0014; implementing a decision-making block at a computing system to select a task block to control vehicle).
Regarding claim 40, Tiwari et al. disclose wherein: the training data comprises one or more second labels for the driving log data, the second labels for the driving log data comprising a vehicle action at the respective time steps (See paragraph 0014; the autonomous operation mode and manual operation mode re considered as the first and second labels for the driving log data);
and the operations further comprise training a machine-learned vehicle action model using the training data including the one or more second labels, wherein the trained machine-learned vehicle action model is configured to be executed by the autonomous vehicle operating within the environment (See paragraph 0014; the autonomous operation mode and manual operation mode are considered as the first and second labels for the driving log data; paragraph 0016 trained machine model learned vehicle action model executed by autonomous vehicle within the environment).
Allowable Subject Matter
Claims 23-24, 35, are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art fails to disclose further comprising: providing a portion of the training data as input to the machine-learned model; determining, based on an output of the machine-learned model, in response to receipt of the portion of the training data provided as input, and relative to the first labels utilized as ground-truth data, an accuracy level of the machine-learned model; and updating the machine-learned model based on the accuracy level.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ferguson et al. (U.S. Patent No. 8,855,849) discloses an autonomous vehicle may be configured to detect objects based on known structures of an environment. The vehicle may be configured to obtain image data from a sensor and be configured to operate in an autonomous mode. The image data may include data indicative of a known structure in the environment. The vehicle may include a computer system. The computer system may determine, based on a first portion of the image data, information indicative of an appearance of the known structure. The computer system may determine, based on a second portion of the image data, information indicative of an appearance of an unknown object in the environment. The computer system may also compare the information indicative of the appearance of the known structure with the information indicative of the appearance of the unknown object and provide instructions to control the vehicle in the autonomous mode based on the comparison.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERTRUDE ARTHUR JEANGLAUDE whose telephone number is (571)272-6954. The examiner can normally be reached Monday-Thursday, 7:30-8:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramya P Burgess can be reached at 571-272-6011. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GERTRUDE ARTHUR JEANGLAUDE/Primary Examiner, Art Unit 3661